DEV Community: Lux Tech Academy The latest articles on DEV Community by Lux Tech Academy (@luxacademy). https://dev.to/luxacademy https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3862190%2F25e07cd4-5be8-4877-baf3-b57838d3d612.jpeg DEV Community: Lux Tech Academy https://dev.to/luxacademy en Power BI Projects: Global Superstore Analytics Lux Tech Academy Sun, 05 Apr 2026 12:04:14 +0000 https://dev.to/luxacademy/power-bi-projects-global-superstore-analytics-47mh https://dev.to/luxacademy/power-bi-projects-global-superstore-analytics-47mh <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8c0zihzid8ztln4ebffp.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F8c0zihzid8ztln4ebffp.png" alt=" " width="800" height="533"></a></p> <p>Online shopping has become increasingly important, especially post COVID-19 pandemic, which limited physical store visits. Businesses must now rely heavily on data to understand customer behavior, optimize product performance, and improve operational efficiency.</p> <p>Using the <strong>Global Superstore dataset</strong>, the following are structured <strong>Power BI projects</strong> that demonstrate end-to-end analytics capabilities.</p> <p><strong>Downloading Dataset:</strong><br> Downloading the Dataset:<br> Follow the link below to download the .csv file with the dataset. Open this <a href="https://github.com/LuxDevHQ/Data" rel="noopener noreferrer">link</a>, <a href="https://github.com/LuxDevHQ/Data" rel="noopener noreferrer">https://github.com/LuxDevHQ/Data</a>, and download Global_Superstore2.csv.</p> <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5fa6terzzao7pvrcujk8.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5fa6terzzao7pvrcujk8.png" alt=" " width="800" height="198"></a></p> <p>Please note that this is a private repository under the LuxDevHQ organization, so you need to be added to LuxDevHQ.</p> <p>If you are not added, WhatsApp your username to 0796448232 to be granted access.</p> <h3> <strong>Stage 1 Data Engineering &amp; Ingestion</strong> </h3> <h2> Objective </h2> <p>Build a scalable data pipeline by moving raw data into a database.</p> <h2> Scope </h2> <ul> <li>Import CSV/Excel data into: <ul> <li>SQL Server / MySQL / PostgreSQL</li> </ul> </li> <li>Create structured tables: <ul> <li>orders</li> <li>customers</li> <li>products</li> </ul> </li> <li>Connect Power BI to the database</li> </ul> <h3> <strong>Stage 2: Data Cleaning &amp; Transformation</strong> </h3> <p>Prepare clean and reliable data for analysis using Power Query.</p> <h2> Key Tasks </h2> <ul> <li>Handle missing values (Postal Code, Discount anomalies)</li> <li>Remove duplicates (Order ID + Product ID)</li> <li>Fix data types (dates, numeric fields)</li> <li>Create derived columns: <ul> <li>Delivery Days</li> <li>Profit Margin</li> </ul> </li> <li>Standardize categories and country names</li> </ul> <h3> <strong>Stage 3: Data Modeling (Star Schema Design)</strong> </h3> <p>Design an optimized data model for performance and scalability.</p> <h2> Model Structure </h2> <ul> <li> <strong>Fact Table</strong>: Orders </li> <li> <strong>Dimension Tables</strong>: <ul> <li>Customers</li> <li>Products</li> <li>Geography</li> <li>Date Table</li> </ul> </li> </ul> <h2> Key Deliverables </h2> <ul> <li>Proper relationships</li> <li>Calendar table creation</li> <li>Optimized filtering</li> </ul> <h3> <strong>Project 4: Customer Analytics</strong> </h3> <p>Analyze customer behavior and profitability.</p> <h2> Key Questions </h2> <ul> <li>How frequently do customers purchase?</li> <li>Do high-frequency customers generate more revenue?</li> <li>Are they more profitable?</li> <li>What is the profit margin across customer segments?</li> <li>Which customer segment is most profitable each year?</li> <li>How are customers distributed geographically?</li> </ul> <h3> <strong>Stage 5: Product Analytics</strong> </h3> <p>Evaluate product performance and pricing impact.</p> <h2> Key Questions </h2> <ul> <li>Which country generates the highest sales?</li> <li>What are the top 5 profit-making products yearly?</li> <li>How does price affect sales?</li> <li>Is there a relationship between price drops and increased sales?</li> </ul> <h3> <strong>Stage 6: Operations &amp; Delivery Analytics</strong> </h3> <p>Assess logistics and delivery performance.</p> <h2> Key Questions </h2> <ul> <li>What is the average delivery time across countries?</li> <li>Which regions experience delays?</li> <li>How does delivery performance impact customer satisfaction?</li> </ul> <h3> <strong>Stage 7: DAX &amp; Advanced Analytics</strong> </h3> <p>Develop advanced analytical capabilities using DAX.</p> <h2> Key Deliverables </h2> <ul> <li>Core KPIs: <ul> <li>Total Sales</li> <li>Total Profit</li> <li>Total Orders</li> </ul> </li> <li>Customer segmentation logic</li> <li>Profit margin calculations</li> <li>Time intelligence (YTD, YoY)</li> <li>Moving averages and ranking</li> </ul> <h3> <strong>Stage 8: Dashboarding &amp; Reporting</strong> </h3> <p>Build interactive and insightful dashboards.</p> <h2> Dashboard Pages </h2> <h3> Executive Overview </h3> <ul> <li>KPIs (Sales, Profit, Orders)</li> <li>Sales trends</li> </ul> <h3> Customer Insights </h3> <ul> <li>Segmentation</li> <li>Revenue contribution</li> <li>Geographic distribution</li> </ul> <h3> Product Performance </h3> <ul> <li>Top products</li> <li>Price vs sales analysis</li> </ul> <h3> Operations Dashboard </h3> <ul> <li>Delivery time</li> <li>Shipping performance</li> </ul> <h3> <strong>Stage 9: Business Insights &amp; Storytelling</strong> </h3> <p>Extract and communicate actionable insights.</p> <h2> Example Insights </h2> <ul> <li>High-frequency customers are not always the most profitable </li> <li>Some regions have high sales but low margins </li> <li>Discounts reduce profitability </li> <li>Delivery delays vary significantly by region </li> </ul> <h3> <strong>Final Outcome</strong> </h3> <p>By completing this project, you will: </p> <ul> <li>Perform data cleaning and transformation </li> <li>Design efficient data models </li> <li>Apply advanced DAX analytics </li> <li>Create professional dashboards </li> <li>Deliver business insights effectively </li> </ul> analytics dataengineering microsoft sql